This project supports research to improve methods for estimating absolute and attributable risk and collaborative studies to estimate such risks for various cancers. We developed a variety of methods for the estimation of absolute risk from population-based case-control studies and applied these techniques to projecting the individualized risk of breast cancer based on data from the Breast Cancer Detection Demonstration Project (BCDDP). We developed appropriate variance estimators for absolute risk that take all sources of variability into account and published graphs that allow practitioners to estimate absolute risk together with confidence intervals to assess uncertainty. We also developed new methods to estimate absolute risk from a two-stage population-based case-control study and applied these methods to incorporate mammographic density, which was only measured on subsets of cases and controls in the BCDDP, in breast cancer risk models. We also developed software for inference on population attributable risk from case-control data and an approach to estimating population attributable risk based on an arithmetic mixture model that allows the effect of exposure to be modified by another risk factor. We collaborated on a variety of studies to estimate population attributable risk, including: a study in Missouri to estimate risk for lung cancer from former smoking, elevated saturated fat, history of benign lung disease, and domestic radon exposure; a study of domestic radon exposure in West Germany; a study of family history, parity, and other breast cancer risk factors based on the National Health and Examination Survey (NHANES I) cohort; a study of smoking, coffee consumption, and dietary patterns as risk factors for bladder cancer in Northern Italy; a study of dietary factors for gastric cancer in Northern Italy; and a study of smoking, elevated protein intake, and other risk factors for renal cell cancer. Measurements of DNA content by flow cytometry did not provide independent prognostic information for the survival of patients with ovarian epithelial carcinoma.